The Deep Learning Nanodegree program was one of the first Udacity programs built as a direct and immediate response to the very latest advancements in the field of AI, and as such, it was an early and unprecedented opportunity for aspiring learners to master valuable and in-demand deep learning skills. Deep learning is such a dynamic and rapidly-advancing field, and it has been a delight to see so many students learn and grow with this field. Thousands of students have graduated from the program, and many have gone on to great careers at companies like OpenAI, NASA, and more—not to mention the amazing personal projects our graduates continue to build!

As researchers learn more about deep learning, and as the technology evolves, our curriculum must advance as well. The rapid pace of change in this field means that we’re constantly updating and enhancing the content in this program, in order to consistently ensure our students always have the best educational experience possible. Staying up-to-date with a field this innovative isn’t easy, but our commitment to doing so is a big part of why this program is so special.

In this post, I’m going to share some exciting new updates to our Deep Learning Nanodegree program: the use of an additional deep learning framework: PyTorch, a new section on Model Deployment, and a new lesson on Image-to-Image transformation!

Deep Learning with PyTorch

There are a number of frameworks available to help you construct and train deep learning models. The most popular are TensorFlow, Keras (which has been wrapped into TensorFlow), and PyTorch. TensorFlow was the first widely-used deep learning framework, supported by Udacity’s first deep learning course released in January of 2016. Just over a year later, PyTorch was released as an open-source project from Facebook, and it quickly caught on with deep learning researchers. In this update of the Deep Learning Nanodegree program, we’ve built our content with PyTorch as well as TensorFlow. Both frameworks are used extensively in industry and are surrounded by active communities. By including PyTorch and TensorFlow in our curriculum, our goal is to prepare students for success anywhere in the industry.

Model Deployment

Udacity’s Nanodegree programs are designed for you to learn valuable skills that are used in industry, today. As more and more companies look to build AI products, there is a growing demand for engineers who are able to deploy machine learning models to global audiences. To help prepare you to take advantage of this demand, and qualify for roles of this kind, we are adding a new section on model deployment and model serving to the Deep Learning Nanodegree program. Here, you’ll get hands-on experience deploying and monitoring a model using PyTorch and Amazon SageMaker. By teaching these essential skills, we are preparing our students to be indispensable members of AI product teams.

Image-to-Image Translation

This course will cover the latest in deep learning architectures used in industry, including architectures called Pix2Pix and CycleGAN. These models approach the challenge of image-to-image translation tasks, such as transforming images from winter to summer or turning sketches into realistic images.

We also focus on Generative Adversarial Networks (GANs). GANs are a relatively new invention, introduced by Ian Goodfellow in 2014, and Udacity has partnered with Ian to provide instruction on this unique class of artificial intelligence algorithms.

The opportunity to partner with experts in both industry and academia is an important benefit for our students, as it enables us to provide you with the most in-depth looks at the latest technologies. Ian’s 2014 GAN paper spurred on even more GAN research, and we’re excited to have another expert on board to enhance your learning experience. Jun-Yan Zhu is a researcher at MIT’s CSAIL, and he’ll be teaching you about his recently-published work on CycleGANs.

The Power of Deep Learning

Deep learning is constantly evolving, and this field has shown continuous growth over the past few years. There has never been a better time to start learning about the deep learning models that are changing the way we work. It is really up to us as learners and teachers to shape how this technology advances, and we can do so by learning about the latest deep learning techniques and coding our own models. If you are curious about the subject, and are interested in applying deep learning skills to personal or professional projects, then this course is for you!

Cezanne Camacho

Cezanne is a Udacity Curriculum Lead. She is an expert in computer vision with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she has applied computer vision and deep learning to medical diagnostic applications.